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Typical sales compensation schemes have a lower limit, a minimum sell price (or margin percentage) below which the sales representative will not earn a commission. This makes obvious good sense: if the company is not going to earn a profit from the sale, the sales agent shouldn’t either.

It’s also common that businesses sometimes need to waive this common-sense requirement. When a sale represents a first break at a coveted major buyer, when a sale is expected to lead to additional purchases or service work or other additional revenue, it can make better sense to take the initial sale at lower than usual profit – sometimes even at a loss – in view of the longer-term payback.

Both of these expectations ought to incorporate delivery schedules. No company is in the business of delivering products before they are ordered (but nice try, Amazon); most business-to-business (B2B) companies with a sales force that receives commission cannot offer instant delivery. Usually if a sales rep makes a commission in a B2B context, the product must either be configured or selected from a complex range of options that require specialized knowledge to make a good choice.

In other words, the sales rep needs to know the product.

Expanding on that, the sales rep needs to know the capabilities of the product and of the company – how they support the product in terms of training, warranty service, spare parts availability, and whatever else goes along with that particular product’s complexity that justifies paying a sales rep.

Barring exceptional and pre-approved circumstances, a sales rep who sells a product at no profit has done a disservice to the company, neglecting his duty, and does not deserve to be paid.

A sales rep who sells a product requiring faster-than-usual delivery has also done a disservice to the company and does not deserve to be paid—again, except in pre-approved situations. I would argue that it is actually worse than selling below cost.

Selling below normal margins has an obvious, easily countable cost; fewer dollars come in. The number is right there in black and white.

Rushing an orders sets off a chain reaction of costs, most of which are not measureable or are not clearly attributable directly to that one order. Rush orders usually first affect the salaried staff, whose pay is rolled into the overall operating overhead of a company. Depending on the business and the size of the rushed order, responding to a rush order can take up time from the highest-paid people in the company. Again: sometimes this is the right course of action. But the endorphin rush of responding to an “emergency” makes it all too easy for business managers to forget that the best use of their time is planning the company’s future. Any time an upper-level manager gets involved in the present, it is a signal that the day-to-day processes of the company are not meeting the needs of the business; it is the managerial equivalent of a machine break-down.

Managers rushing around saving the day inevitably impacts the workers, too, who must halt their usual practice (ideally following a reliably excellent process) to accommodate the boss’ special request. This might immediately lead to overtime and measurable, attributable costs incurred. But more of the cost is likely to hide in the shadows: skipping inspection or maintenance or documentation, something where the cost of the sacrifice won’t be felt immediately, and won’t even matter if the skipped step is skipped just this once.

It is never just this once.

Once the sales rep has learned that he can delight his customer with the exceptional performance, he’ll ask for it again – he’ll need it again. Once managers learn that they feel like heroes when they orchestrate the impossible, they’ll want to do it again. And once production workers learn they can skip a step without noticeable repercussions, they’ll do it whenever they need to.

A rush order is not really like a house on fire, despite the frequent reference to “putting out fires.” A house fire is catastrophic; nobody just “gets used to it.” A rush order is more like a small stone hitting your windshield. Nobody dies. Maybe it makes a small chip or crack. Maybe it doesn’t even make a mark! But if it happens all the time, you will lose that entire windshield. So how often is too often?

Generally a business won’t improve until its customers demand it. Prices don’t fall unless sales are slipping. Quality doesn’t improve unless orders are lost. The wake-up call that faster delivery times are needed will generally come in the form of these rush orders, these expedites that shake the tree from bottom to top.

Managers must recognize that an expedite is the operating failure of the machinery they are responsible to keep running.

Electrek has published a company-wide email from Elon Musk. It contains pithy proscriptions to cheer the soul of anyone wearied by corporate bureaucratic processes. Here are two:

– Communication should travel via the shortest path necessary to get the job done, not through the “chain of command”. Any manager who attempts to enforce chain of command communication will soon find themselves working elsewhere.

– A major source of issues is poor communication between depts. The way to solve this is allow free flow of information between all levels. If, in order to get something done between depts, an individual contributor has to talk to their manager, who talks to a director, who talks to a VP, who talks to another VP, who talks to a director, who talks to a manager, who talks to someone doing the actual work, then super dumb things will happen. It must be ok for people to talk directly and just make the right thing happen.

This is so great! Now nobody has to get permission from their boss to do anything! If you think it’s the right thing to do, do it!

One thing to keep in mind, though. People sometimes disagree about what the right thing to do is. Sometimes you can try several options and see what works best, but this is rare, particularly if money is tight or time is tight. We can probably assume time is tight at Tesla.

For example, let’s start with another part of Elon’s letter:

Some parts suppliers will be unwilling or unable to achieve this level of precision. I understand that this will be considered an unreasonable request by some. That’s ok, there are lots of other car companies with much lower standards. They just can’t work with Tesla.

Let’s say there is a supplier who hasn’t been meeting expectations. Someone from Sourcing decides to find another supplier. Someone from Purchasing negotiates with the existing supplier to reach a mutually acceptable agreement. Someone from Design decides to change the design so the part is not needed. Someone from Manufacturing figures out a quick, efficient way to modify the part during assembly to resolve the issue while still meeting cost targets.

Is it realistic that all of these solutions could be found within the same time frame? No. Is it realistic that four different people start working on different, overlapping solutions? Yes. And they probably each need to work with three or four other people, discussing options, agreeing on changes, determining who will do what, just to figure out what the end results (both costs and benefits) would be from pursuing their initial idea. So how will this group of sixteen people (an implausibly small number) react when one solution is chosen?

Four people will feel great. Twelve people will feel that they have wasted their time and efforts, and they will be less willing to help in the future – both within their sub-group of four, and within the larger group of people who were involved in looking for a solution.

So who makes sure we don’t have sixteen people working on the same problem in an uncoordinated overlapping or contradictory fashion? Who makes sure that the solution to Problem A does not cause new problems with the solution to Problem B?

To use a different example, if we are driving around town and looking for a parking spot, who decides whether we turn right or left, or continue on straight through the intersection?

That’s called “chain of command.” Chain of command means that Person A gets to decide about Topic X and we will all live with their decision.

What Elon is suggesting here is fully possible, within boundaries. Maybe any decision that costs less than $1,000 dollars, just do it – costs more and takes more time to debate it than to try, and then try something else if it doesn’t work. But ideally you would want to make sure the decision isn’t going to have a lot of unexpected consequences on other people, and, generally speaking, managers are more likely to have the experience and information to recognize if a decision is going to unintentionally affect other people too.

Speaking of boundaries, check out this part of the letter:

All capital or other expenditures above a million dollars, or where a set of related expenses may accumulate to a million dollars over the next 12 months, should be considered on hold until explicitly approved by me. If you are the manager responsible, please make sure you have a detailed, first principles understanding of the supplier quote, including every line item of parts & labor, before we meet.

Ok, let’s just try to get a very rough sense of perspective on this.

Elon wants to build 5,000 cars per week. Call it 50 weeks per year, leaving two weeks out for factory upgrades. That’s 250,000 cars per year, so any decision having an impact of $4 per car has to be approved by the CEO.

Tesla spent about $4.2 billion USD last year on automotive operations, up from about $2.6 billion in 2016. That is an increase of about 4.5 million dollars every day of 2017. Four and a half decisions per day is not too much to ask of a CEO, but usually these decision are not simple one-time snap decisions like: cream or no cream?

Tesla had 37,543 employees in 2017, according to Statista.com, and in the letter Elon mentions adding 400 people per week for “several weeks”. So let’s say 40,000 employees and figure 200 work days (Elon probably works 7 days a week but that is not likely to be representative); anything that adds a cost of 13 cents per day per employee has to be approved by the CEO. That’s either one decision for the whole year, or one thirteen-cent decision every day, or perhaps one 90 cent (roughly) decision every week, which would “accumulate to” a million dollars in 12 months.

All of these examples are only outer limits, of course; if every single employee were making decisions every day that crossed this threshold, Tesla would be spending even more money. But, for context, in one place I worked every employee could spend up to $500 on a single transaction (or for a single purpose), and it actually is quite limiting in a business context. It wasn’t enough to pay for an hour of time from the videographer.

If no manager at any level can spend a million dollars, limits will be correspondingly lower for an assembly line supervisor than for a Chief Office or Vice President. People might even have to get approval from their, well, chain of command.

Speaking of that, let’s see what happens when you don’t go through your chain of command:

I have been disappointed to discover how many contractor companies are interwoven throughout Tesla. Often, it is like a Russian nesting doll of contractor, subcontractor, sub-subcontractor, etc. before you finally find someone doing actual work. This means a lot of middle-managers adding cost but not doing anything obviously useful. Also, many contracts are essentially open time & materials, not fixed price and duration, which creates an incentive to turn molehills into mountains, as they never want to end the money train.

Clearly Elon didn’t approve these arrangements, so I have to assume that someone just got the job done, did the right thing, maybe even ignored a “company rule” that was “obviously ridiculous in a particular situation.”

You get the sense, reading this comment, that Elon thinks time and materials contracts are just obviously ridiculous to a common sense understanding. So why would anyone set up a contract that way?

Well, “time and materials” would be a good fit if you didn’t have a really detailed idea of what all the necessary work would be to get to the end result you wanted, or you don’t know all of the precise details that define every aspect of the end result. If you’re not sure or you’re not capable of explaining it well to your supplier, they aren’t going to want to promise to delivery whatever exactly it is you want for a fixed price. But if you tell them, “Hey, let’s figure it out together as we go, and we’ll pay you for your time and materials,” well, now you can get somewhere.

“Figure it out as we go” can be an appropriate strategy where time is of the essence, and it’s pretty easy to see how someone working at Tesla might feel that way.

Going back for a second look at a piece already quoted:

If you are the manager responsible, please make sure you have a detailed, first principles understanding of the supplier quote, including every line item of parts & labor, before we meet.

“First principles understanding” is an excellent approach for figuring out whether something is ultimately possible. It is a terrible way to estimate how successful your first attempt will be. And “detailed […] every line item” is kind of the opposite of “first principles,” in the sense that it has to include all of the things that are not “first principles,” like taxes, insurance, overhead, risk, etc.

What Elon has accomplished already is astonishing by any human measure. The production quality and quantity of cars from Tesla has not been perfectly on track with expectations, but nobody else has done better starting from zero like Tesla. Tesla’s burned through a lot of cash, but other people have gotten less done for the money. Elon’s been a jerk to at least some people, but other people are jerks and also totally ineffective.

I am not brave enough to bet against Elon Musk.

He’s going to die some day, I’m sure of that, and I’m pretty sure that before that happens at least one of his business ventures will collapse; his empire will be split up, and some or all will go on under different management. Eventually Alexander the Great turned back, you know, but I wouldn’t be the one to go looking to pick a fight with him.

Still, if I had to describe Tesla so far, I’d say that with company, Elon has accomplished astonishing things by spending an astonishing amount of money. Now he’s asking his company to keep accomplishing astonishing things but stop spending astonishing money. I don’t think it works like that. He’s asking for each individual to make independent, quick decisions, each decision reflecting the collective intelligence of the company – but don’t hold meetings. I don’t think it works like that.

I am not envious of anyone working at Tesla right now.

One last quote:

Walk out of a meeting or drop off a call as soon as it is obvious you aren’t adding value. It is not rude to leave, it is rude to make someone stay and waste their time.

This I agree with. This is the kind of meeting you set up when you’re not sure who needs to be involved or exactly what decision needs to be made, when you aren’t fully prepared but you just hope to figure it out in the moment. I’ve been guilty of setting up those meetings myself.

On the whole, it is better to include people than to leave them out. But that needs to be balanced with allowing people to decide for themselves that they aren’t needed after all, without any recriminations.

“So, please join the movement to ban productivity from medicine. We are not producing anything. We are caring for patients who need our full attention.” This is the concluding appeal from “It’s Time to Ban ‘Productivity’ in Medicine,” by Robert Centor, M.D.

This type of rhetoric is designed to imply that anyone who disagrees hates people – or, in this case, patients. Often it is children or mothers or women in general. But the basic formula is a false dichotomy, where if you do things my way, you care for people; if you do anything else, all you care about is getting rich while harming others.

Profit is not the enemy; at least not any and all profit. “Profit” at its simplest is just a way of saying thanks that is tangible: the doctor gets paid for the health care.

Businesses today can be so multi-layered and convoluted that it’s easy to imagine the “profit” only means that some man in a suit somewhere far away from the real work gets to buy a new yacht. While this can be one of the outcomes, and we can agree for the sake of the discussion that it’s unfair, it’s not the simplest or most basic meaning of profit. In the simplest form, it means that the guy in the suit can pay the doctor for his work and not lose money. This is good, because if the guy in the suit loses money and expects to keep losing money he quits paying the doctor.

Then the doctor would need to get his or her money directly from you. And the doctor would need to be really sure that he or she is getting enough money to pay off the medical school debt, and also make all those grueling years of school worthwhile. It’s hard to be sure of that unless you get that guy in the suit involved again. Paying for the doctor’s schooling just makes this harder to talk about but doesn’t really change the facts: whoever teaches the doctor needs money, so if the patient who pays taxes to pay for the schooling to be provided for free to the doctor and pays more taxes to pay the doctor so that the actual doctoring is free – it’s just more complicated, but at the end, if the whole thing is not making a profit, it is losing money and will have to shut down.

There are big, hard to manage problems in health care in the USA today. And it’s a lot harder to measure whether doctors are doing a good job than it is to measure whether widget-builders are building widgets.

Those two truths do not justify this self-entitled appeal to “ban” productivity measurement in health care.

The complaints that Dr. Robert Centor has are basically the same complaints that everybody everywhere in every job has when their job gets measured. Measurement always introduces the possibility of perverse incentives. When you measure how many widgets the widget-builders make, they have a tendency to make more; when you emphasize the measurement of how fast they can make widgets, they make more at the expense of product quality, safety, and improvement to the product itself and the production process. Improper measurements of “productivity” were one of the major problems in American manufacturing that were called out by Toyota (and other Japanese manufacturers) when they overtook American car manufacturers in product quality and reliability.

Thus, the Toyota Production System never recommends measuring how many units are produced as a good measure by itself. At the most basic level, the Toyota Production System admonishes you to understand how many are actually wanted and not to build too many. Along with that, if the widgets you make do not work they do not count as “units produced” – quality matters. Furthermore, if you haven’t done your research to make sure the widgets do something valuable to your customers, anything you make is waste no matter how expertly you make it.

Transferred over to health care, the first thing we can observe is that measuring how many patients doctors see is probably the same as measuring how many junky unsafe cars factory workers can produce. It’s measuring the wrong thing. How much only matters if you are making the right stuff. So if we are going to learn any lessons from manufacturing productivity – any of the lessons on productivity and quality from the last five decades or more – we need to figure out how to measure whether doctors are doing the right stuff before we try to measure how much of it they do.

Measuring quality of care is a lot harder than measuring quantity of care, no doubt.

But even measuring quality of manufactured goods is harder than it sounds at first. Most of the cars sold in the world today don’t need to be able to drive any faster; a car that can go 125 mph is not worth any more than a car that can go 95 mph to most people. But a car seat that is comfortable for hours at a time is probably worth paying for. That’s harder to figure out, but it matters more for the real meaning of product quality (what customers are willing to pay for).

I’ve said it before but I probably can’t say it too many times. Measuring quality of care is hard. But think for a minute about the opposite. What if we don’t measure care quality at all, ever?

Well, if we don’t ever measure the quality of care, that gets us right back into pre-scientific medicine: “Sounds to me like you need to swallow some cat dung. Hope it works for you!”

Instead, although measuring care quality is hard, and we can’t do it perfectly, we need to measure care quality and keep finding new and more accurate ways to measure it.

But what about costs? Leaving aside the vast and unhelpful complications of the current health insurance system, if I go to Dr. Jones for corrective lenses and he prescribes glasses that adjust my eyesight to 20/20 and charges me $200, but (for science!) I then go to Dr. Smith and he prescribes glasses that also bring my sight to 20/20 and charges me $500, which is a better deal?

Okay, so the same work for less money is better productivity.

Tell me again why we shouldn’t measure productivity?

What we need to do is have continuing conversations about how to measure real productivity. Yes, there is definitely a possibility that measurements could lead to perverse incentives; managers need to recognize that and account for it. Managers who emphasize simple measures of “productivity” in factories will see quality go down, and DID see quality go down, and lost their business to competitors who paid more attention to quality. If you are a health care provider working for an administration that measures “productivity” only in quantity, the problem is not the measurement: it’s your management. And they will lose their business. (Provided they are allowed to lose it; which means they have to lose money if they don’t satisfy patients; which requires patients are allowed to choose providers whom they think give better care; but that problem is for another discussion.)

I was recently asked how I would apply Lean Six Sigma concepts to reduce patient visit time, if given the chance. Part of my answer was: who says patients want their visit time reduced? Most people I know want to spend more time with the caregiver and less time in the waiting room, so “reducing time” is a poorly-stated goal.

The root cause of the problem Dr. Centor is observing is not the use of measurements; it’s that the same interest group gets to determine limits on what the patient needs and how much the doctor will get paid for it. Insurance companies can limit what they will “accept” in billing, which effectively means what the doctor is allowed to do (to be paid). If patients paid directly, they would pay more to spend more time with the caregiver. Since the payers are managing by numbers, not by experience, they need countable things like tests prescribed; add that with a very natural desire to make sure you’re getting bang for the buck (which is, in this case, measureable health care per dollar of reimbursement), and presto! You now have an incentive to prescribe as many expensive treatments in a short period of time as possible.

Misuse of measurements will always be a risk, especially when money is involved. The right approach is not to stop measuring altogether, as Dr. Centor suggests, but to think carefully about what you measure and think twice as hard about what it means. Problems with measuring performance do not apply in a fundamentally unique way to health care providers; there are problems with measuring engineers, HR communications, and office workers in general; right on down through to factory workers. Managing by the numbers makes no more sense than driving your car by staring at the speedometer. You need the speedometer to keep your senses in check, but you need to stay aware of the context to know whether 45 MPH is too slow on the highway or too fast in a school zone.

In the case of Dr. Centor, his rallying cry was addressed pretty well by commentator David Pogge, who wrote in part: “However, the implied alternative appears to be a system based on the notion that every provider knows what is ‘optimal’ and their work will drift towards providing that optimal service if external pressures are removed. This idea is either self-aggrandizing or naive. Healthcare providers are human, and therefore they are as flawed, selfish, shortsighted, lazy, and prone to misjudgment as all other human beings. To think otherwise suggests that one has never actually worked with real people in a real healthcare setting.” People, whether patients or doctors or factory workers, cannot be managed well by numbers alone. But people, even doctors, need numbers to provide objective feedback. Sometimes the numbers will be misleading, and sometimes the numbers will be irrelevant; but medicine without measurement is just superstition. A doctor should know better.

(This is the fourth post in a series; see earlier posts I, II and III)

Here we come to the most significant chapter of the saga.

The MRP system in place at the company at that time tracked three dates for a given order (more precisely, for a line on the order):

Requested date, when the customer asked for it;

Promise date, when the factory currently expects to be able to provide it;

Due date, the date by which the work should be done inside the factory, used to sequence all of the factory work.

Due date is not necessarily the same as the promise date. First, if the factory was running behind overall, due dates could be missed. You wouldn’t want to move only one due date for one order into the future, because it would lose its place in line versus all of the other past-due orders. Also, depending on how the entire system is set up, the production control system that coordinates by due date might not include activities after assembly (transfer to shipping, pack for shipping, consolidate with other items on order, etc.), and in fact in this case the system did not track specific activities for shipping. So a one or two day gap between due date and promise date would allow time for shipping activities, or might be used to provide a small buffer in case of minor delays.

Promise date is not always the same as requested date, because customers can request things faster than they can be built – because of how much they want, or because of a long lead time to get a special component, or because the factory overall is already busy.

Given these three different dates, and the fact that all of them can be changed, what does it mean to be “on-time”?

Customer Request Date would make the most sense. But customers can ask for unreasonable things – they can ask for giant orders of specialized product to be delivered the next day. Orders came in by many means – by fax, by phone, or through an early use of the Internet called Electronic Data Interchange (EDI). Not all of those methods reliably required the customer to specify a request date; if no value was provided, the system defaulted to next-day.

So a significant portion of the request dates were quite out of reach.

Using Promise Date is no good because the promise date can be changed by the factory, so being “on-time” to promise date is a simple matter of updating the field before shipping. Promise date needs to be changeable by the factory so that an indication of what is expected to happen can be provided, but measuring that will only measure how reliably people can change the promise before delivering on it.

Therefore a modification to the system was ordered which stored the first promise date given, the Original Promise Date.

But it turns out that the factory more or less controls the generation of the first promise, as well, so before too long the Original Promise Dates were soaring out into the future, and the factory’s performance to Original Promise Date was improving.

What to do? Well, no problem in people management can be solved by measurement alone. But it’s pretty obvious that the right thing to do in this case would have been to accurately capture what the customer actually wants – the customer being the person willing to pay for what you can provide.

This factory with the on-time delivery problem was not a pizza factory. If someone called asking for pizza, that person would not be a customer. It was not a space ship factory, and if someone called asking for a space ship they would not be a customer. It was also not in the business of providing most products to most customers on a next-day basis, and so most people requesting that would not be asking for what the factory could provide; they would not be customers.

The basic steps toward measuring on-time delivery include knowing what “on-time” means, which means understanding what the customers you can reasonably serve are willing to pay for. That implies two basic points, and a third point follows along:

Customer request date must be provided. It could be provided once and applied to an entire order, or provided for each line on the order, or the order could be held as “pending” until a customer service agent could follow up and obtain the information. There would be some expense to make the changes to the system. There would need to be some conversations with customers in the habit of getting next-day shipment at no extra charge, when available, merely by lazily doing nothing, that they now have to ask for what they want.

For most customers in most cases, request dates that would be moderately challenging should require an added expedite fee. If the customer is not willing to pay for the service of quick shipment, they are not a quick shipment customer.

If you charge extra for what your competitors provide for free, you will lose business. For important deals where an expedite fee would lose the business but it might be possible to provide the order in time, it may be appropriate to waive the fee. This would require diligent investigation on the part of the sales representative regarding what the customer actually wants, and equally diligent investigation as to exactly what the factory is currently able to provide. While this extra effort might seem unwelcome, it should be better for all parties than conversations about why delivery is late – and the intolerable frequency of those conversations was the basis for the whole improvement effort in the first place.

The best situation of all is, of course, to provide what the customer wants instantaneously, at no extra charge. But it is better to only promise what you can actually deliver than to take money for a service (fast shipment) that you cannot provide.

The growing power of Amazon compels a bit further discussion. Amazon got where it is today partly by providing lower prices than its competitors, including delivery. Amazon delivers faster than others, and often for free, and their prices are lower. Many of Amazon’s competitors figured they were not able to do this without losing money. As far as I know, Amazon’s never solidly disproven this suspicion; for years they’ve been so busy spending money growing it’s never entirely certain they’re making profit on their core distribution business. They’ve also muddied the water with the Prime membership program and it’s interestingly flexible definition of two-day shipping, and the possibility of quietly boosting prices for complacent, regular shoppers (hard to prove without having account access to a large sample of customers).

But how Amazon did it is largely irrelevant. If you are certain they subsidized money-losing shipping with investor cash and growth-hype marketing, go do the same. If you think they hide their shipping costs in a membership program, go do the same. Advertising “free shipping” when the shipping cost is baked into the prices is a well-known marketing ploy, and at this point convincing investors to pour money into a unprofitable but growing business is also well-known. In the end, whether the money is coming from end-user customers or investor customers, someone is paying; and whether you are providing actual shipment of widgets or dreams of getting rich off of a unicorn stock, something is being provided.

So, to insist: customers pay for what you provide. If they don’t pay or you don’t provide it’s not a lasting business.

The possibility of considering investors as customers leads us to another interesting wrinkle. It turns out that investors are the customers of every publicly-traded company. (What follows is just a quick review of the classic agency problem; skip five paragraphs to “From the lofty heights” if you don’t need it.)

The investors are represented by the Board of Directors. Directors can be replaced by a sufficiently large contingent of shareholders (investors). To keep their jobs, directors try to keep investors happy.

Directors must approve the compensation plan for the top management. They do whatever they can think of to ensure that the top management will take actions that make the shareholding investors happy.

Shareholders of any size can easily sell stock on any given day. Sell Company A, buy Company B. In general, then, shareholders have no reason to want Company A to do well over the long term. As long as the share price for Company A has increased enough to pay off any costs associated with buying and selling the stock, big share holders always can (and, in a sense, “should”) dump the stock for a better offer at any time.

So investors always want to know that, compared to other stocks with a comparable risk of losing value, the stock of Company A is doing really well right now.

That’s why publicly traded companies tend to want to have really swell results every month, and especially every quarter and at the end of every year. Most businesses have natural ups and downs; ice cream sells better in the summer than in the winter. Peppermint candy sells better for Christmas than for Independence Day. But publicly traded companies do whatever they can to overcome the natural ups and downs of their business, because if they aren’t doing great right now they might get dumped for some other company that’s doing a little better.

From the lofty heights of the stock market this looks like a jolly little beauty pageant. From the ground floor of a factory, it looks like deciding to ship the newer order for plain vanilla ice cream before the older order for vanilla ice cream with organic Oolong tea leaves sourced exclusively from certified fair-trade villagers in Nepal, because the donkey is lame in Nepal and the tea leaves are late and we can ship the plain vanilla order this month, making the profits in this month a little bit better. We could ship the older order for the organic Oolong vanilla two days from now when the tea leaves get in, and if we ship the plain vanilla now we will be out of vanilla for three weeks, making the Oolong order even later. Too bad; that “customer” will just have to wait, because the real customers are the investors and they want to see good profits this month.

In short, resources get borrowed from an older order that can’t ship in order to fill a newer order that can ship. There are many possible reasons why an order might be hard to ship – it requires special components, the customer wants the entire order to ship complete and other items on the order are late, the customer hasn’t passed the credit check, and more.

Clearly this violates the principle of first in, first out (FIFO), or in Toyota Production System terms, continuous flow. It also probably irritates you on general principle; nobody instinctively likes how it sounds. But, as they say, nobody wants to know how the sausage is made; but that doesn’t stop them from eating it. Very likely you will find some practices that make you irritated or uncomfortable behind every nice thing, particularly the nice things that seem remarkably cheap, remarkably convenient, or otherwise mysteriously available to you without any particular effort that you know about.

Let’s ignore the irritation factor. The problem with this scenario goes deeper than just making the organic Oolong vanilla customer wait longer than necessary. This kind of out-of-sequence activity causes rippling chaos that spreads far beyond the visible consequences when the decision was first made. Every part of the production process is designed to work in a basically first-in, first-out way; even if it hasn’t been adopted as part of imitating the Toyota Production System, than at least because it is awfully hard to build the order for five years from now since you don’t know what they are. At some level, you add things to your production plan as you become aware that they are wanted.

In our entirely made-up example of the organic Oolong tea vanilla, perhaps the tea leaves need to ferment, and they were planned to ferment next week when the tea leaves came in; but they can’t be fermented too long before being used, so if we aren’t making the Oolong vanilla until three weeks further out we’ll need to delay fermenting the tea leaves. But that will cause a conflict with fermenting the mint leaves for the mint that was supposed to run that week, so now we have to move that. And then we’ll have to reschedule the chocolate for the mint chocolate, and that will affect the schedule for the chocolate in the chocolate peanut butter; and so on.

It is of course possible to deal with these kinds of disruptions, because they happen whether you deliberately cause them or not. But it should be readily apparent that there is a difference between dealing with problems you tried to prevent versus deliberately causing yourself those problems. Dealing with one specific chain of events is not too daunting, but when the problems occur on multiple components for multiple products on multiple orders over many weeks, and the problems can affect each other, the consequences multiply. It’s not eight plus eight, it’s eight times eight.

The Toyota Production System principle of flow is meant, in part, to reduce those self-inflicted complications. Going from eight root-cause problems (eight times eight, 64) to seven root cause problems (seven times seven, 49) reduces your total problems by 15. Reality is more complex than that simple math, in good was as well as in bad ways, but the principle holds: problems multiply, and keeping things as steady and predictable as you can will make it much, much easier for to work on your real problems without being distracted by all the consequences of problems you caused for yourself.

The crowning irony for this particular company’s efforts to improve on-time delivery was the decision that had been made some time prior to designate certain customers as so important to the business that their orders would ship ahead of all others. Because these were by nature customers representing a significant portion of the total business, their orders tended to affect most of the products. If a particular product was running past-due and a production schedule drawn up to get the oldest orders down from two weeks old to two days old, and then just as the products went to shipping a priority customer order came in, that priority order might use up all of the product. The order could come in and ship out in less than a day, leaving everyone confounded who thought that the old orders would be caught up (the customers themselves, the customers service agents talking to the customers, the factory production planners, etc.).

The priority customers were also fairly likely to be major distributors who carried significant inventory of their own and might not need the product nearly as urgently as the smaller customers with older orders.

As already mentioned, production control software is designed to plan to build things basically in the order they were requested. Typical production software is designed to plan to produce in the future, not to explain why production ran a certain way in the past, so it was not particularly easy to tell why the product meant to ship on old orders somehow disappeared without clearing up the old orders.

And if the product was one model in a family with shared components, and a second production run was made after the first disappeared to really clear up the old orders, that might use up shared components meant for other orders. Again, the problem spreads in a ripple.

All of the issues discussed in this article are some form of rushing: accepting a next-day shipping “request” without checking to see if it is paying business; shipping out of sequence to boost end-of-period revenue; or shipping out of sequence to flatter a key customer (rather than investing in improving basic ability to ship to all customers faster). Although not possible to get an accurate measure on how big the ultimate impact of these rushing efforts were on overall on-time delivery (because production computer system plan future production action, not explain past production action), the principle of problems multiplying and the demonstrated success of the Toyota Production System (and similar philosophies, when rigorously followed) give a pretty good indication that the self-inflicted injuries were severe enough to justify stopping those practices.

Ultimately, to achieve on-time delivery, it is important to stop rushing.

In the previous installment we saw how neither the human operators nor the computerized information system had a clear, timely understanding of what inventory was meant to be in a specific location or reserved for a specific purpose. The material handling processes in place did not conform to the principle of visual control (“mieruka”). Although the system was intended as a form of kanban, it did not conform to the principles of kanban because the role of the spare parts pickers had not been considered, and there was no visual feedback for them. This could also be considered a lack of mistake-proofing (“poka-yoke”), and potentially a lack of “5s”, specifically Sort and Set In Order, again from the perspective of the spare parts picker who had no means to distinguish parts available for shipment and parts reserved for production. It also should be considered a dangerously incomplete IT systems implementation of a process, but a reliance on IT systems to “make things work” is itself not understanding the principle or benefit of visual control.

Today’s episode has to do mainly with leveling (“heijunka”). Leveling is always a sore point in implementing a Toyota Production System based process because it is in direct tension with the principle of responding to customer demand. There isn’t any case where customer demand is level and unwavering; even for oxygen, although we are always breathing, as we need more oxygen when active or excited than when resting.

But leveling is fundamental to quality, or more specifically to being able to observe whether results are in line with expectations. If you expect your production line to produce 100 units every day, all of the workers should be working at the same pace every day, and you should minimized if not eliminate mistakes caused by rushing. Further, without having a standard pace for the entire process, it is all but certain that some parts of the process will be able to operate faster than others, and the faster steps will accumulate work in process behind the slower processes. The extra inventory allows the faster process to “hide” errors by recovering while the slower process catches up, a short-term success that obscures the potential for small problems to grow into big ones.

In this particular factory, leveling production was made difficult by the large number of different end products, some of which had constant demand (although varying in total volume) and others with only occasional demand. If the occasional-demand items would politely take turns the production could be leveled and still tied directly to demand, but of course this serendipity was not reliably present.

So the basic system for moving components from storage to the production line relied upon a crew of material handlers to monitor several assembly lines and restock all of them as needed. Most materials we provided via a “two-bin” system: in concept, you start with two full bins. When the first is empty, it is taken away and refilled before the second is used up. (Sometimes more than just two bins are used.) Because the bin quantities were not all aligned to have enough parts for the same number of finished products, and because some parts were used in common across several end products (but other components were not), the rate at which parts would need to be replenished was not reliable. A change-over from assembling one product to another would mean refilling many bins simultaneously; if several lines needed to change over at around the same time, work might back up considerably.

Further, since production lines might build a number of different products and not all were built equally quickly, and not all lines were always producing, there was drastic variation in the level of support needed from material handlers. At the end of the month, and especially at the end of the quarter, pressure to build as much as possible went up sharply.

To compensate for the changing work load on the material handlers, the more senior members of the assembly team were also authorized to retrieve components from storage. When the urgency to maximize production was greatest, production planners (typically responsible for balancing available component parts with current final product demand, a desk-based job) would assist in moving components to the line so that none of the potential builders would lose time moving parts.

The production people were trained, expected, and performance measured in terms of production of complete products. Precision in counting and handling component parts was not part of how their success was evaluated in any direct way.

When parts arrived at the last minute, material handlers (full time or temporary fill-ins) would retrieve the parts directly from the receiving dock, before they had been counted, quality checked, or received into the system at all. Combined with the other uncertainties afforded by the process, the question of how much of an item was available and where it was stored was always unclear.

Several component parts were springs, or spring-like items, which could easily become entangled with one another. These parts, and all small, lightweight parts, were shipped in bags or boxes. Taking parts out of storage, or out of a bin during the assembly process, is very likely to involve pulling out more parts than desired; disentangling was not a straightforward process, and it could be expected that one or more parts could drop off, roll, or bounce into places unknown. These were always small, cheap parts; when pressure was on to build, build, build, it hardly seemed worthwhile to spend time chasing runaway parts. There’s always tons more in the bin.

Ultimately it was not clear how often dropped parts caused shortages versus parts not ever being shipped. Most small, light parts were shipped loose packed in bags or boxes and the count of pieces in each bag or box could vary. The suppliers would write on the box the count, but there was no routine verification by weight. Checking by weight as parts were removed was complicated by the light weight of the parts; they weighed little enough that plastic or cardboard would count as significant number of parts.

It would be relatively straightforward to establish the weight of the parts, establish a minimum stock by weight, and write off the continual shrinkage due to dropped parts or undetectable short shipment. But the parts were bought and tracked by each, and the each-count was always off, and so production was halted for small, cheap parts as often as it was for large expensive parts.

Better than allowing dropped parts and potentially short shipments would be designing purpose-built containers using rods or grooves to keep parts in a consistent orientation, clearly marked with a weight corresponding to an exact count. While it would require a modest investment to design and produce the specialized containers, and possibly working with the supplier to help establish a reliable process to transition the parts from their production equipment to the containers, the payback in reduced uncertainty would have paid back such costs.

For want of a nail, the shoe was lost;
For want of the shoe, the horse was lost;
For want of the horse, the rider was lost;
For want of the rider, the battle was lost;
For want of the battle, the kingdom was lost;
And all from the want of a horseshoe nail.

One of the most frustrating, recurrent problems that would cause late delivery at this company was a part shortage on an assembly line because the parts had been shipped out as replacement spare parts.

This was not a true root-cause problem because if the total count of parts on hand were accurate, the most harm that could be caused would be a slight delay as parts were moved from one place to another. But, through a combination of several different system configuration decisions and limitations, it was quite common for the same item to be stored in more than one location, and quite common for the location-specific inventory count to be wrong even if the the total count were right, and quite common for the system to indicate that inventory should be taken from a place where it actually no longer was.

How did it get so bad?

The simplest way to provide component parts, widgets let’s say, for assembly into a finished product (gizmos) is to keep large quantities of widgets on the shelf and pull some out of inventory when you need to build a gizmo.

The Toyota Production System teaches that it is not good to have widgets sitting in boxes on the shelf. There are many reasons to consider this wasteful – the extra time it takes to put things up on the shelf in storage and then get them back down when needed, the overhead costs of paying for the floor and roof and shelving for the inventory, the risk that at some point the design of the gizmo will change and the widgets will no longer be useful, and so on. American business managers will usually mention the cash tied up in the inventory that can’t be used for other purposes; I consider this is the least important reason from a Toyota Production System perspective.

Under the Toyota Production System, the worst thing about having many widgets on the shelf is that they will allow you to continue on as if nothing is wrong even if any number of things go wrong: your customers order more than you expected, you break more than you expected while trying to use them, your supplier is later than expected, and so on. A problem you don’t notice is a problem you don’t fix. Since inventory will cover for almost any problem, it encourages people not to fix any problem–it makes it difficult to tell that there is any problem at all.

It would be best if the widgets appeared exactly when needed, and no sooner. Reality being what it is, nobody has managed to apply this theory throughout their entire supply chain. But, if you want to be like Toyota, one of the first things you will do is direct your factory to use a “pull” system rather than a “push” system.

A “push” system is like wood in wood chipper, gravel on a screen, water in a tub, or paper in a paper shredder. The work gets loaded on in a big batch and you try to get through it as quickly as possible. Your job is to get rid of the work and the faster you do it and the more you do the better job you have done.

A “pull” system is a bit like a vending machine or a grocery shelf with a spring loaded pusher. Right up in front, clearly available and ready to take, is one item; as soon as you take it another one appears. Of course in a vending machine the whole load is put in all at once, but in concept a “pull” system has exactly one of everything ready to immediately replace one of whatever is taken.

It is impossible to have a perfect end-to-end pull system in a world where there is weather and friction and, ultimately, time. But as an ideal concept it is a great reminder that making more than is wanted is waste. Make exactly what is wanted, when it is wanted, and no more.

The conventional, “common sense” way to run a factory is to order 1,000 widgets (the widget factory makes them in batches of 1,000 and you get a better price per each if you order in that quantity) and put them on your shelf. When you want to make 50 gizmos you create a production order and 50 widgets are taken off the shelf and brought over to the gizmo assembly line, and then the gizmo assembly line tries to build 50 gizmos as fast as possible. It is a “push” system because the assembly line reacts to having inventory dumped on it by building as fast as possible.

In an idealized “pull” system, there is one finished gizmo sitting at the end of the line. The last worker on the assembly line has an almost-finished gizmo, and each of the other workers has a gizmo in a successively less-finished state. When a customer orders a gizmo, the finished one is picked up off the end of the line and handed to them (or shipped to them). As soon as the finished gizmo is picked up off the end of the line, another gizmo is immediately finished and delivered, and every in-process gizmo moves forward one step, including the step where the widget is used.

To approximate this ideal process, you can store the widgets right there on the assembly line so that the widgets are removed from “storage” one at a time, by the worker on the line, as they are needed.

Production control software was traditionally not designed around that kind of thinking. Production software is used to consuming all of the component widgets when the order to build gizmos is first released. Clever people came up with a work-around whereby the computer system would be told when a complete gizmo was finished, and since a gizmo was finished the system could then correctly infer that one of everything that goes into a gizmo must have been used (including one widget). Rather than consuming the inventory in advance, the system effectively backtracks to correct its own records of how many widgets are still on the shelf. (“Backflushing” is a common term for this backwards flow of information.)

Now, all that was just to set the stage for one of the more interesting problems that came up. Some years before I had become involved, the company had decided that widgets needed as replacement parts should be shipped from the factory making gizmos, rather than from a separate spare-parts warehouse. Why keep the same stuff in two different places? Why ship twice, first to the warehouse and then to the customer?

But in this factory’s version of the “pull” system, widgets were moved in small batches from storage to the assembly line, to approximate as much as possible a one-piece pull system. Since it wasn’t literally a one-piece flow system, sometimes there were more parts in the bin than would actually be needed. On the other hand, for larger quantity builds, the bins would be refilled several times from stock. The computer system was only told after the fact, when all of the gizmos were built, to remove the corresponding number of widgets from inventory.

So when a customer wanted a spare part (or two, or ten, or two hundred), and when they didn’t find the parts in the warehouse, the parts picker would go to the assembly line and take the parts. Then the assembly line would run out of parts and not be able to finish making gizmos.

Fundamentally this was an inventory accuracy problem. The production control system was still checking to make sure there was enough widgets in total to meet all of the needs. But, because it was neither a true one-piece flow system nor a conventional “push” system where all the inventory on the line was truly committed for a production build, it was never visually clear to a parts picker which parts were “really” needed for a build versus just moved to the line to follow the rules of the “two-bin” system.

That it was not visually obvious to the spares parts picker is an immediate red flag from a Toyota Production System perspective. But the “Lean Manufacturing” improvements had been implemented for manufacturing, without consideration for the spare parts process. Basic problems with inventory control were amplified, both in terms of the actual physical effect but even more importantly in the frustration and ill-will the misalignment of processes caused between assembly workers and spare parts pickers. With neither clear visual process control nor up to date and accurate transaction records, it became impossible to tell where the problem started.

The whole mess was a combination of problems, beginning with the batch-based rather than one-piece backflushing, compounded by the keeping inventory in more than one place, and complicated by shipping spares out the same facility and inventory as production. But it all could have worked if the inventory handling had been flawless. Instead, for many parts, it was difficult to tell how many parts actually were in a package from the moment they first arrived in the building, and it didn’t get any easier after that. This is an example where one of the most fundamental Toyota Production Systems principles, “set in order,” was lacking. I will revisit the topic of parts handling and presentation in a later post.

This is the first part in a multi-part series about on-time delivery. If you are familiar with manufacturing (light machining and assembly), all the topics in this post will seem quite mundane. But they deserve to be mentioned because ordinary problems with ordinary solutions can still be part of the chaos even when there are some special causes. Part II describes the inventory chaos resulting from using visual control systems inconsistently across overlapping processes. Part III describes how lack of work leveling leads to inconsistent responsibilities and lack of accountability. Part IV describes how an emphasis on metrics rather than fundamental customer relationships, and on immediate results rather than process capability, skewed efforts to improve.

I spent a significant amount of time trying to help one company improve its on-time delivery. My primary responsibility was to identify causes for late deliveries. Some of the causes were pretty straightforward:

Inventory inaccuracy resulting in component shortage

Quality non-conformance requires rework or scrapping part

Traffic jams in production processes

Supplier late delivering to factory

Since the nature of these problems was pretty ordinary, possible improvements are also not hard to imagine. Inventory accuracy could be greatly improved by locking up inventory and restricting access to authorized personnel who are trained and held accountable to always update the system on how much inventory was released and for what purpose. Although there was a fairly limited number of people who were “usually” responsible for transferring inventory from storage to point of use, there was quite a large set of people who might do it to help out – on second or third shift, when many assembly lines all needed replenishment at the same time, or for any number of other real or imagined reasons. Consequences of restricting access might include delaying some assembly lines if the demand for parts at a given point in time were too great for the authorized staff to handle, or paying authorized staff to stand around and do nothing during periods when demand for materials were lower than peak.

There were also a surprising number of cases where inventory accuracy was hampered by material packaging and presentation deficiencies: pieces dumped in bulk into plastic bags with no clear indication of count, or a count indicated but no corresponding weight with which to check the count. Some parts were packed in bulk even though they were highly prone to hooking and catching on one another, making it quite easy to take more than needed and drop some during handling. More often than not, it was difficult or impossible to tell how many parts were supposed to be in a container at any point in time. Imposing packaging requirements would likely result in an increased component cost.

Quality issues for parts produced in house were, like most quality issues, the result of process variation. Contributing causes might include the condition of the tools or fixtures, contamination of fluids, or unfinished components not to specification. Standard practices for quality control were known and discussed, but the recurrence of issues is a telling indication that corrective actions did not reach to actual root causes. Symptoms of not addressing the root cause include if the corrective action involves rebuking an individual, increasing manual inspections with the addition or alteration of inspection methods or tools, or other changes which would cause the process to take longer and must be done by the worker (not automated equipment) without any change to the way the worker’s performance is measured or evaluated by their immediate supervisor. In other words, instructing the worker to get the same amount of production but follow steps that take longer to do.

Traffic jams occur when the cascading effect of a problem affects later jobs which themselves have no problem, but are started late and finish late due to the problems on other jobs.

Supplier issues, whether late delivery or poor quality, generally have the same types of causes and possible solutions. Much as with internal employees, the main thing that matters in the long run is how suppliers are held accountable. Informing suppliers that you are displeased will have little result if there are not increasing consequences, starting with charges for nonconforming material and ending with discontinuation of the business. Dropping a supplier for quality or on time delivery issues can result in higher costs for buying from higher performing suppliers.

Often the purpose of hierarchy is confused with meritocracy. In a hierarchy, some people hold authority over others. In a meritocracy, people deserve the authority they have because they use the authority well. Ideally the two are perfectly combined, and people in higher positions fully merit the authority that they hold. But as we all know from our own infallible opinions, not everyone who has authority deserves to have it; not every hierarchy is a meritocracy.

Even a hierarchy which is not a meritocracy can be useful.

Imagine four passengers survive a plane crash in the wild. Each has equal survival skills and each is equally uncertain about where the closest civilization can be found. Each guesses a different direction. What would be best for the group? Travelling one hour in a single direction under one leader before switching out and travelling in another direction under another leader? Obviously the group would be better off picking one direction and sticking with it.

This is the fundamental purpose of hierarchy: to remove uncertainty by reducing possibilities to decisions.

Hierarchy cannot accomplish this unless the hierarchy is known and respected. It is the function of the hierarchy itself that must be respected, not the capabilities of each person in it. That the person higher up the hierarchy makes the decision in any cases of dispute or uncertainty is intrinsically necessary for hierarchy to have any value in removing uncertainty.

When hierarchy is not followed, uncertainty proliferates. I watched one CEO hire managers but continue to give instructions directly to employees, without passing it through their manager. Employees learned that any instruction given by their manager could be overridden at any time with new instructions, so they regarded instructions as one possible course of action among many unless the instruction came from the CEO. Employees who had been around for a while were used to this pattern, so new managers never had a chance – all they could do was add more possible courses of action to the ones employees were already considering. The employees all had a notion of what it means to be a manager, so they would generally try to comply with manager directions if it seemed possible; but any time the demands were too many or too contradictory they would default to either what they knew for certain the CEO wanted or what in their own judgement seemed best.

A senior manager who bypasses a middle manager forces that middle manager to be worse than useless. A useless person adds nothing; a worse than useless person subtracts something. A manager who is bypassed by his boss cannot reduce uncertainty for his employees but adds to it by giving additional input that may or may not be let stand by the senior manager, and takes up his employee’s time asking them what they have been told to do by the senior manager.

In another organization, the general manager of a factory had reporting responsibility to a vice president of manufacturing and a vice president of sales. These two VPs had different priorities. For one, the highest priority was increasing on-time delivery; for the other, the highest priority was reducing cash tied up in inventory. A very high-performing organization can accomplish both, but the fastest way to make progress on either of those goals is to sacrifice the other. Over the course of several years the factory would whiplash back and forth, approximately once a quarter: a few months of squeezing out inventory wherever possible, which resulted in deliveries slipping out when the least unexpected event caused components to be unavailable; then a few months pushing for delivery on schedule, causing accumulation of inventory to account for unexpected disruptions.

A matrix organization is a fundamentally bad idea because it reverses the most useful function of a hierarchy. Rather than having a single superior to resolve uncertain situations, additional uncertainty is produced by the possibility of disagreement between authorities. The matrix organization was invented out of a recognition that sometimes engineering skills are needed to accomplish sales goals, and it was somehow imagined that the only way to have access to skills within an organization is to be the boss of a person having those skills. But this is immature at all levels; respectful people who communicate clearly can obtain assistance from people whom they do not control. This ought to be even more true of people at higher levels in the organization; if your VP of Engineering is useless at supporting your VP of Sales, why is he your VP of Engineering at all?

Furthermore, the matrix organization solution doesn’t scale, as there are quite often more than two relevant interests. If making everyone with a legitimate interest a “dotted line” supervisor helped, an organization would become a dense thicket of “dotted line” responsibility and most employees would find that they must treat anyone higher up in the hierarchy as if they were their own manager. Too often employees do feel this way; but in such organizations very little real change occurs, and the organization survives on the inertia it gathered in leaner days to keep producing enough value to survive.

Hierarchy has a specific and real value, and it is a mistake to think of hierarchy as merely a reward system: you do a good job, you get a promotion. A job well done can be rewarded with more pay, or in other ways. A hierarchy is not necessary to have a reward system. Likewise, if merit were equally obvious to all, a hierarchy would not be necessary because people would spontaneously agree on the best option. And a hierarchy does not require that all possible decisions be decided at a higher level than the question originated; generally, an organization will work faster and more effectively if decisions are made lower in the hierarchy.

But at some point uncertainty is inevitable; there will be significant doubt about which choice is best, and the consequences will be significant. It will be impossible or grossly ineffective to attempt more than one option. Perhaps more than one choice will be effective. The only way you reduce the infinite possibilities to decisions, actions and results is by allowing one person in a group to have the final say in decision making.

I’ve seen Marketing expected to manage these business success metrics:

Market share

Profit margin

Sales forecasting

To support these goals, Marketing is generally supported out of the following costs:

Payroll for employees

Media & collateral

Public Relations

Trade Shows

Training

Advertising

In two of the companies I have worked with, Marketing felt hampered in its efforts to reach its goals due to product lead times. In both cases “Lead Time” was not formally considered a Marketing Metric, and in both cases “Inventory” was not considered a Marketing cost.

This seems to me to be a fundamental organizational defect. Delivery lead time is a competitive consideration in many markets (I might dare say most markets), and it’s widely recognized that inventory can be substituted for lead time, and lead time for inventory. This equivalence is a fundamental theory of the Toyota Production System.

It’s true that the need for inventory below the shippable SKU level is partly determined by the manufacturing process capability. Manufacturing clearly owns the responsibility to continually improve the reliability and speed of the manufacturing process. But it still seems quite achievable for the responsible Marketing representative to say, “We have X inventory position in order to support Y delivery expectation.” If challenged as to why so much of X inventory is in WIP (not yet shippable), it should be fairly straightforward to show the inventory required to buffer against the supply chain performance.

As all production and supply chain managers know, a great deal of the need for inventory comes from variance between the demand forecast and the actual demand. It’s also sometimes been my experience that Marketing (or Sales) wishes to divert inventory that was built for Customer B in order to satisfy Customer A – but will still sometimes unabashedly complain about the lateness of fulfilling Customer B’s order. Sometimes these sacrifices have to be made; but Marketing should be paying out of their own pocket.

Simply put, Production and Supply Chain should have fiscal responsibility for inventory covering supply chain and manufacturing process variance, and Marketing should have fiscal responsibility for inventory covering demand variance. Somewhere out in the wide world I’m sure it is so; but I wonder why it is not the rule everywhere.

Alas, the influx of jobs has not boosted wages for the region’s forklift drivers and order-fillers. In the years since Amazon opened its doors in Lexington County, annual earnings for warehouse workers in the area have fallen from $47,000 to $32,000, a decline of over 30%

In a story from its January 2018 print edition, The Economist takes Amazon to task for underpaying its employees, probably. What “underpaying” means is unclear, coming from a publication founded, one assumes, on economic principles. In the online edition the article carries the suggestive sub-head “Is the world’s largest online retailer underpaying its employees?” Then, after five paragraphs of implying that Amazon somehow systematically causes people to lose income, The Economist finally allows that:

Another possible explanation for Amazon’s pay is its reliance on unskilled workers with minimal qualifications. David Neumark of the University of California, Irvine, who has written about the impact of Walmart’s growth on retail wages, says Amazon’s highly automated warehouses may not require as many workers who can, say, operate a pallet jack.

Hmmm, maybe.

A forklift carrying a load can total about eight tons (16,000 lbs or about 7,200 kg). The actual weight can vary quite a bit depending on what’s on the pallet and how high the pallet will need to be lifted, among other things, but just to get a sense of the weight, that would be around four times the average car in the USA. Heavier objects are harder to stop, and forklifts often move in spaces where there are literally only inches (two or three) to spare between the vehicle and something that isn’t meant to be hit. Forklifts do move much more slowly than cars on public roads, but when it comes to damaging things, 16,000 lbs will crush most things even if it is moving slowly.

Forklifts are mainly used to move pallet loads, and pallets are usually stacked with cases or cartons.

Amazon is in the business of shipping usually small quantities of diverse things going to diverse places. While Amazon might consolidate parcels going to the same distribution point on a shared pallet as the last stage in the shipping process before loading onto a truck, most of the handling of objects in an Amazon warehouse involves people handling the items directly, one at a time or one case at a time. Rather than sending a forklift to go fetch a pallet of corn chips to be sent to a corner store, Amazon will dispatch a person to pick one fitness tracking band and one pair of earbuds.

In short, Amazon’s warehouses will be designed mainly for human traffic. E-commerce and the mail order catalogs of yesteryear will general have this human-traffic based warehouse design.

But most of our warehouses have been built for traditional supply chains, which mostly move pallet loads of goods: pallets upon pallets of frozen chickens from the slaughterhouse to the grocery distribution warehouse, and then from there one pallet for this store, one pallet for that store, etc, etc.

So it is quite likely, not just possible, that on average, Amazon employs fewer forklift drivers than the pre-Amazon average warehouse.

It is also quite likely that a person required to safely operate a 16,000 lb vehicle in tight space would be paid more than a person required to walk, read labels, and pick things up.

The complain that Amazon lowers wages for “warehouse workers” is like complaining that McDonald’s lowers wages for chefs, because after a McDonald’s opened on the same block as Le Snootier’s, average wages for restaurant workers on that block went down. Nobody at Le Snootier’s got paid any less; but people who were not qualified to work at Le Snootier’s got paid more than they were getting before.

This sort of insinuation that Amazon is somehow taking away from people something that they already had is familiar work for rabble-rousing, any time someone needs a rabble roused, but it is particularly deplorable for a publication calling itself The Economist. That highly relevant possibility that perhaps apples are being compared with oranges was admitted somewhere in the article does not absolve them of headlining the question and leading off with all kinds of spurious facts.

The “average” wage for warehouse workers may have been lowered because of the increase in the number of workers who are doing jobs that have always been priced at the lower end of the range. If you are trying to examine the effect that Amazon has on “warehouse workers,” why not look at the total wages paid to all warehouse workers after Amazon opened a warehouse in a given area? Or if you are trying to examine the effect on the community, why not look at total count of people employed, or average income per capita? Those measures may very well have gone up. But we don’t know, because The Economist didn’t consider them worth examining.

Is Amazon virtuous in all of its dealings? I hardly think so. But let it be found guilty of crimes it has committed, rather than being found guilty because it has done well for itself.